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This listing doesn't post a salary. Most similar roles pay
$154,600–$200,312.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
The Senior Lead Software Engineer-AI Foundation Services joins the Chief Data & Analytics AIML Data Platforms team to build secure, cloud-native platform capabilities for GenAI and traditional ML at enterprise scale. You will lead the hands-on delivery of infrastructure components, translating business requirements into technical designs and production-quality engineering deliverables. Key responsibilities include developing reusable integrations, reference implementations, and GPU/training/serving baselines while driving adoption of AI-assisted engineering practices. You will utilize a technical stack including Kubernetes, containers, CI/CD, and Infrastructure-as-Code, with proficiency in Python, Java, or Go. The role focuses on solving complex problems regarding model hosting, inference optimization, and production reliability, ensuring that AI/ML workloads meet strict performance, security, and scalability standards through automated testing, observability, and secure-by-design engineering practices.
Design, build, and optimize AI Foundation Services infrastructure for GenAI and traditional AI/ML platforms.
Translate business requirements into technical designs, implementation plans, and engineering deliverables.
Develop reusable engineering assets including reference implementations, deployment templates, and onboarding guides.
Build and integrate cloud-native platform capabilities using Kubernetes, CI/CD, and Infrastructure as Code.
Implement durable, maintainable code solutions for production platform capabilities that support multiple application teams.
Drive adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
Manage non-functional requirements including performance, scale, reliability, security, and production readiness reviews.
Resolve complex technical issues by collaborating with product, security, infrastructure, and application teams.
What we're looking for
Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience designing, building, testing, and operating production software systems, distributed services, or platform capabilities.
Practical experience with AI/ML platform capabilities, model serving, model hosting, data access patterns, or infrastructure services supporting AI/ML workloads.
Experience developing cloud-native applications using Kubernetes, containers, CI/CD, and infrastructure-as-code.
Proficiency in one or more programming languages such as Python, Java, or Go to deliver high-quality production code.
Experience translating business requirements into technical designs, implementation tasks, delivery milestones, and operational support plans.
Working knowledge of performance engineering, production reliability practices, secure-by-design engineering, and responsible AI use in engineering workflows.
Experience building GPU-backed model hosting platforms, LLM serving patterns, or creating reusable developer enablement assets (preferred).